Business

This dataset list business information with average business rating, along with category of the Business. Business Train Data

Review

Review is main dataset here, it holds link to Users and Business and the corresponding Text Reviews are listed along with ratings. Review Train Data

User

Users Holds all the information about user, average rating of the Users given so far, count of the review and different count of Votes on “Cool”, “Useful” and “funny”.

User Train Data

Checkin

Checkin information is round the clock and 7 days data of the Customer’s checkin. Column name start with hour-Day here hours [00 to 23]- [0 to 6]

Checkin Train Data

Business Categories

df_main_train_business_long<- df_main_train_business %>%  
  unnest("bus_categories"= df_main_train_business$bus_categories ,.id= "id") %>% .[,c(2,3,4,5,6,9,10,1,8)] # Star column at index at 8

df_main_train_business_long

# All Business Catgories 
df_main_train_business_long %>% filter (bus_open== TRUE) %>% select (business_id,bus_categories,bus_state) %>% group_by(bus_categories) %>% dplyr::summarise(Count=n()) %>%   ggplot(aes(x = bus_categories, y = (Count))) +
         geom_point()+
          theme(axis.text.x=element_text(angle=90, hjust=1))+
    xlab(label = "Business Categories")

# Business Categories with 350+ Business
df_main_train_business_long %>% filter (bus_open== TRUE) %>% select (business_id,bus_categories,bus_state) %>% group_by(bus_categories) %>% unique() %>% dplyr::summarise(Count=n()) %>% arrange(.,desc(Count)) %>% filter (Count >= 350) %>%   ggplot(aes(x = bus_categories, y = (Count))) +
         geom_bar(stat = "identity") +
          theme(axis.text.x=element_text(angle=90, hjust=1))+
    xlab(label = "Categories with 350+ Business")+
  geom_label(aes(label = Count),  color="white" , fill= "black",nudge_y = -0.02)

## Focus Category 
gl_focus_category <- c("Food","Restaurants","Shopping")

Business States/City

# All Business By State
df_main_train_business %>% filter (bus_open== TRUE) %>% select (business_id, bus_state) %>% group_by(bus_state) %>% dplyr::summarise(Count=n()) %>%   ggplot(aes(x = bus_state, y = log(Count))) +
         geom_bar(stat = "identity") +
          theme(axis.text.x=element_text(angle=20, hjust=1))+
    xlab(label = "Business State")+
  geom_label(aes(label = Count),  color="white" , fill= "black",nudge_y = -0.02)

# All Business City
df_main_train_business %>% filter (bus_open== TRUE) %>% select (business_id, bus_city) %>% group_by(bus_city) %>% dplyr::summarise(Count=n()) %>%   ggplot(aes(x = bus_city, y = Count)) +
         geom_bar(stat = "identity") +
          theme(axis.text.x=element_text(angle=90, hjust=1))+
    xlab(label = "Business City")+
  geom_text(aes(label = Count), colour = "blue", fontface = "bold" ,position = position_stack(vjust = 1.1),check_overlap=TRUE)

# All Business City by Focus Category
df_main_train_business_long %>% filter (bus_open== TRUE,bus_categories %in% gl_focus_category ) %>% select (business_id, bus_city,bus_categories) %>% unique()%>% group_by(bus_city,bus_categories) %>% dplyr::summarise(Count=n()) %>%   ggplot(aes(x = bus_city, y = Count, fill=bus_categories)) +
         geom_bar(stat = "identity") +
          theme(axis.text.x=element_text(angle=70, hjust=1))+
    xlab(label = "Business in City by Focus Category")

Rating

# All Business Categories by Rating
df_main_train_business_long %>% filter (bus_open== TRUE,bus_categories %in% gl_focus_category ) %>% select (bus_categories,bus_city, bus_stars) %>% unique()%>% group_by(bus_city, bus_stars) %>% dplyr::summarise(Count=n()) %>%   ggplot(aes(x = bus_stars, y = Count,fill= bus_city)) +
         geom_bar(stat = "identity") +
          theme(axis.text.x=element_text(angle=90, hjust=1))+
    xlab(label = "Stars Rating by City")

  # geom_text(aes(label = Count), colour = "blue", fontface = "bold" ,position = position_stack(vjust = 1.1),check_overlap=TRUE)



# All Business Categories by Rating
df_main_train_business_long %>% filter (bus_open== TRUE,bus_categories %in% gl_focus_category ) %>% select (bus_categories,bus_city, bus_stars) %>% unique()%>% group_by(bus_city, bus_categories,bus_stars) %>% dplyr::summarise(Count=n()) %>%   ggplot(aes(x = bus_stars, y = Count,fill= bus_categories)) +
         geom_bar(stat = "identity") +
          theme(axis.text.x=element_text(angle=00, hjust=1))+
    xlab(label = "Stars Rating by Focus Category")

# Count of Business by Category 
fct_count(df_main_train_business_long$bus_categories,sort = TRUE) %>% rename(.,c("f"= "Name","n"="Count"))

# library(forcats) 
# 
# plot(fct_count(df_main_train_business_long$bus_categories,sort = TRUE))
# 
# unique(df_main_train_business_long$bus_state)

# Avg Rating Given by User
df_main_train_user %>% select (user_id,usr_average_stars,usr_review_count) %>%
  # group_by(user_id,usr_average_stars,usr_funny) %>% 
  # dplyr::summarise(Count=n()) %>%   
  ggplot(aes(x = round(usr_average_stars), y = usr_average_stars)) +
         geom_bar(stat = "identity") +
          theme(axis.text.x=element_text(angle=00, hjust=1))+
    xlab(label = "Avg Rating Given by User")

Review

library(lubridate)

## 
## Attaching package: 'lubridate'

## The following object is masked from 'package:plyr':
## 
##     here

## The following object is masked from 'package:base':
## 
##     date

# Avg Rating Given by User
df_main_train_review %>% select (rev_stars,rev_date ) %>% 
  mutate(month = month(as_date(rev_date) )) %>% group_by(month) %>% 
  dplyr::summarise(Count=n()) %>%  
  ggplot(aes ( x= factor(month),y = Count)) +
  geom_bar(stat = "identity") +
  theme(axis.text.x=element_text(angle=00, hjust=1))+
  xlab(label = "Avg Rating Given by User")

str_split(df_main_train_review$rev_date[1],"-",n=3,simplify = TRUE)[2]

## [1] "01"